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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You've built a model in Snowflake to predict house prices based on features like location, square footage, and number of bedrooms. After deploying the model, you want to ensure that the incoming data used for prediction is similar to the data the model was trained on. You decide to implement a data distribution comparison strategy. Consider these options and select all that apply:

A) Only focus on monitoring the target variable (house price) and assume that if the distribution of house prices remains stable, the input data distribution is also stable.
B) Use Snowflake's built-in statistics functions to compute quantiles (e.g., 25th, 50th, 75th percentiles) for each numerical feature. Compare these quantiles between the training and incoming datasets and set up alerts for significant deviations.
C) Generate histograms for each numerical feature in both the training and incoming datasets using a Python UDF that leverages libraries like Pandas and Matplotlib. Visually compare the histograms to identify potential distribution shifts.
D) Create a binary classification model in Snowflake that attempts to predict whether a given row of data comes from the training dataset or the incoming dataset. If the model achieves high accuracy, it indicates a significant difference in data distributions.
E) Calculate the mean and standard deviation for each numerical feature in both the training and incoming datasets using Snowflake SQL. Create a Snowflake Alert that triggers if the difference in means or standard deviations exceeds a predefined threshold for any feature.

2. You are working with a large dataset of sensor readings stored in a Snowflake table. You need to perform several complex feature engineering steps, including calculating rolling statistics (e.g., moving average) over a time window for each sensor. You want to use Snowpark Pandas for this task. However, the dataset is too large to fit into the memory of a single Snowpark Pandas worker. How can you efficiently perform the rolling statistics calculation without exceeding memory limits? Select all options that apply.

A) Use the 'grouped' method in Snowpark DataFrame to group the data by sensor ID, then download each group as a Pandas DataFrame to the client and perform the rolling statistics calculation locally. Then upload back to Snowflake.
B) Increase the memory allocation for the Snowpark Pandas worker nodes to accommodate the entire dataset.
C) Explore using Snowpark's Pandas user-defined functions (UDFs) with vectorization to apply custom rolling statistics logic directly within Snowflake. UDFs allow you to use Pandas within Snowflake without needing to bring the entire dataset client-side.
D) Break the Snowpark DataFrame into smaller chunks using 'sample' and 'unionAll', process each chunk with Snowpark Pandas, and then combine the results.
E) Utilize the 'window' function in Snowpark SQL to define a window specification for each sensor and calculate the rolling statistics using SQL aggregate functions within Snowflake. Leverage Snowpark to consume the results of the SQL transformation.

3. You are tasked with building a Python stored procedure in Snowflake to train a Gradient Boosting Machine (GBM) model using XGBoost.
The procedure takes a sample of data from a large table, trains the model, and stores the model in a Snowflake stage. During testing, you notice that the procedure sometimes exceeds the memory limits imposed by Snowflake, causing it to fail. Which of the following techniques can you implement within the Python stored procedure to minimize memory consumption during model training?

A) Implement XGBoost's 'early stopping' functionality with a validation set to prevent overfitting. If the stored procedure exceeds the memory limits, the model cannot be saved. Always use larger virtual warehouse.
B) Reduce the sample size of the training data and increase the number of boosting rounds to compensate for the smaller sample. Use the 'predict_proba' method to avoid storing probabilities for all classes.
C) Write the training data to a temporary table in Snowflake, then use Snowflake's external functions to train the XGBoost model on a separate compute cluster outside of Snowflake. Then upload the model to snowflake stage.
D) Convert the Pandas DataFrame used for training to a Dask DataFrame and utilize Dask's distributed processing capabilities to train the XGBoost model in parallel across multiple Snowflake virtual warehouses.
E) Use the 'hist' tree method in XGBoost, enable gradient-based sampling ('gosS), and carefully tune the 'max_depth' and parameters to reduce memory usage during tree construction. Convert all features to numerical if possible.

4. You are using Snowpark Python to process a large dataset of website user activity logs stored in a Snowflake table named 'WEB ACTIVITY'. The table contains columns such as 'USER ID', 'TIMESTAMP', 'PAGE URL', 'BROWSER', and 'IP ADDRESS'. You need to remove irrelevant data to improve model performance. Which of the following actions, either alone or in combination, would be the MOST effective for removing irrelevant data for a model predicting user conversion rates, and which Snowpark Python code snippets demonstrate these actions? Assume that conversion depends on page interaction and a model will only leverage session id and session duration.

A) Option E
B) Option D
C) Option C
D) Option A
E) Option B

5. You are managing a machine learning model lifecycle in Snowflake using the Model Registry. Which of the following statements are true regarding model lineage and governance when utilizing the Model Registry for model versioning and deployment?

A) Custom tags and metadata can be associated with each model version, enabling detailed documentation and traceability of model development and deployment.
B) The Model Registry provides a central repository to register, version, and manage models, enabling better collaboration and governance across data science teams.
C) Integration with Snowflake's RBAC (Role-Based Access Control) allows for granular control over who can register, update, and deploy model versions.
D) Model Registry automatically retrains models based on scheduled data updates, ensuring models are always up-to-date without manual intervention.
E) The Model Registry automatically tracks the exact SQL queries used to train the model, allowing for full reproducibility of the training process.

Solutions: